276 abilities of mortality between live and dead trees because neither was able to predict any mortal- ity, making both models ineffective and unusable. Once trees were categorized as likely dying within the next three years, this year mortality model may add to fine-tuning management decisions. This model, however, may provide limited clarity in the mortality process. Since it could only successfully predict less than 72% of tree mortality years cor- rectly, the application of this model may not pro- vide tree managers with an effective decision tool. As with other major environmental changes (e.g., drought, development), the interaction between tree location, land use, and mortality can be complex (Iakovoglou et al. 2001; Fahey et al. 2013). Interac- tions between soil type, drought conditions, com- paction, and adjacent land-use may add to the error in prediction of ash mortality by emerald ash borer. However, the models presented here provide initial steps to establishing removal hierarchies within tree management agencies. By using the assessment variables in ModelA DBH, and presence of bark splits), probability of potential mortality can be calculated relatively quickly and with reasonable accuracy. Removing trees that have the highest probabilities of dying within the immediate future (1–3 years), while retaining trees that will live beyond the next three years, would likely result in a potential financial ben- efit of distributing removal costs over several years. and presence of bark splits) or ModelB Acknowledgments. We would like to thank the two anonymous review- ers whose comments helped improve the clarity of this manuscript. This work was supported in part by a John Z. Duling grant from the Tree Research and Education Endow- ment Fund. We would like to thank ongoing cooperation by the Huron-Clinton Metroparks and City of Fort Wayne Parks. Also, we would like to thank Cigdem Gurgur for review of preliminary work for this manuscript. Computational tools to implement these models are available online (http://users. ipfw.edu/marshalj/FSM). (percent crown dieback, DBH, (vigor rating, Clark et al.: Ash Mortality Model Development LITERATURE CITED Crook, D.J., A. Khrimian, J.A. Francese, I. Fraser, T.M. Poland, A.J. Sawyer, and V.C. Mastro. 2008. Development of a host-based semiochemical lure for trapping emerald ash borer Agrilus pla- nipennis (Coleoptera: Buprestidae). Environmental Entomology 37:356–365. de Groot, P., W.D. Biggs, D.B. Lyons, T. Scarr, E. Czerwinski, H.J. Evans, W. Ingram, and K. Marchant. 2006. A Visual Guide to De- tecting Emerald Ash Borer Damage. Natural Resources Canada, Great Lakes Forestry Center, Sault Ste. Marie, Ontario, Canada. Donovan, G.H., D.T. Butry, Y.L. Michael, J.P. Prestemon, A.M. Liebhold, D. Gatziolis, and M.Y. Mao. 2013. 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